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Found 40 papers, 1 papers with code
Advancements in Radiomics and Artificial Intelligence for Thyroid Cancer Diagnosis
The application of AI and radiomics to thyroid cancer diagnosis is examined in this review.
Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina
With the ability to evaluate the patient's health status vis a vis DM complication as well as risk prognostication of future cardiovascular complications, AI-assisted retinal image analysis has the potential to become a central tool for modern personalized medicine in patients with DM.
Current and future roles of artificial intelligence in retinopathy of prematurity
Retinopathy of prematurity (ROP) is a severe condition affecting premature infants, leading to abnormal retinal blood vessel growth, retinal detachment, and potential blindness.
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis
To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes.
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
It breaks down the interpretability process into data pre-processing, model selection, and post-processing, aiming to foster a comprehensive understanding of the crucial role of a robust interpretability approach in healthcare and to guide future research in this area.
An Investigation of Hepatitis B Virus Genome using Markov Models
Our analyses reveal that either APOBEC3 enzymes are not active against HBV, or the induction of G-to-A mutations by these enzymes is not sequence context-dependent in the HBV genome.
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
AI and deep learning are transforming healthcare, offering the potential for single retinal image-based diagnosis of various CVDs, albeit with the need for accelerated adoption in healthcare systems.
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources.
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey
This review article provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery.
AI Framework for Early Diagnosis of Coronary Artery Disease: An Integration of Borderline SMOTE, Autoencoders and Convolutional Neural Networks Approach
The accuracy of coronary artery disease (CAD) diagnosis is dependent on a variety of factors, including demographic, symptom, and medical examination, ECG, and echocardiography data, among others.
Uncertainty Aware Neural Network from Similarity and Sensitivity
In the proposed NN training method for UQ, first, we train a shallow NN for the point prediction.
A Brief Review of Explainable Artificial Intelligence in Healthcare
This paper presents a systematic review of XAI aspects and challenges in the healthcare domain.
Revolutionizing Genomics with Reinforcement Learning Techniques
In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics.
A Critical Review of the Impact of Candidate Copy Number Variants on Autism Spectrum Disorders
Of the remaining 30 regions, we identify 24 regions containing at least one protein-coding genes with brain-enriched expression and nervous system phenotype in mouse mutant and one lncRNAs with both brain-enriched expression and upregulation in iPSC to neuron differentiation.
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets
The superiority of BERT models over other deep models in sentiment analysis is evident and can be concluded from the comparison of the various research studies mentioned in this article.
Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review
Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined.
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence
The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing.
Deep Learning in Spatially Resolved Transcriptomics: A Comprehensive Technical View
In this paper, we provide a comprehensive overview of these deep learning methods, including their strengths and limitations.
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review
We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities.
The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms
In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication(M2M), Train-to-Train(T2T) communication network, Peer-to-Peer(P2P) network.
FCM-DNN: diagnosing coronary artery disease by deep accuracy Fuzzy C-Means clustering model
First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models.
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors
Their positions and directions were exploited for automatic facial expression recognition using different data mining techniques.
Facial Expression Recognition
Facial Expression Recognition (FER)
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Accurate Prediction Using Triangular Type-2 Fuzzy Linear Regression
The current survey proposes a triangular type-2 fuzzy regression (TT2FR) model to ameliorate the efficiency of the model by handling the uncertainty in the data.
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
The tunable-Q wavelet transform (TQWT) is employed to decompose the EEG signals into different sub-bands.
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models
In this step, the DL models were implemented and compared with different activation functions.
GSVMA: A Genetic-Support Vector Machine-Anova method for CAD diagnosis based on Z-Alizadeh Sani dataset
One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis.
Application of artificial intelligence techniques for automated detection of myocardial infarction: A review
Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals.
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction.
Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review
In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities are discussed.
Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods
This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series.
Combining a Convolutional Neural Network with Autoencoders to Predict the Survival Chance of COVID-19 Patients
To show that clinical data can be used for COVID-19 survival chance prediction, the CNN-AE was compared with multiple pre-trained deep models that were tuned based on CT images.
An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works
Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section.
Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images
In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images.
Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data.
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection
Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates.
Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)
We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science.
Automated Detection and Forecasting of COVID-19 using Deep Learning Techniques: A Review
Additionally, a review of papers on the forecasting of coronavirus prevalence in different parts of the world with DL is presented.
Epileptic Seizures Detection Using Deep Learning Techniques: A Review
The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed.
Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review
Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging.
Material Recognition for Automated Progress Monitoring using Deep Learning Methods
Recent advancements in Artificial intelligence, especially deep learning, has changed many fields irreversibly by introducing state of the art methods for automation.
